Publications

My research interests lie at the intersection of the fields of machine learning, cognitive neuroscience, and developmental robotics. More specifically, I am interested in the design of artificial systems that exhibit life-long, motivated, cumulative learning of increasingly complex perceptual and behavioral representations. Such representations, I believe, must be learned in a developmental setting as they are in humans and animals, and so computational architectures inspired by principles of neural and behavioral development in biological systems interest me greatly.

My doctoral research under my advisor, Professor Andrew Barto, involved the development of algorithms for intrinsically motivated learning of hierarchies of skills in artificial agents. We used reinforcement learning as a formalism to explore ways of modeling intrinsically motivated behavior in humans and animals; i.e., behavior that is rewarding for its own sake, rather than because it solves a specific task.

As an undergraduate, I was also involved in research with Professor John Moore and Robert Polewan of the UMass Department of Neuroscience and Behavior. We developed a human eyeblink conditioning paradigm (the Cartesian Reflex Project) for studying the effects of different stimuli (e.g., faces vs. geometric shapes) on cognitive processing time in traditional classical conditioning tasks with voluntary unconditioned responses. My primary contribution to this endeavor was the development of the hardware/software interface and protocol design software used in the paradigm.